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"content": "Extracting biologically relevant information from DNA microarrays is a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of gene expression data, but when analyzing the large and heterogeneous collections of gene expression data, conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been presented as an alternative approach to standard clustering techniques to identify local structures from gene expression data set. These patterns may provide clues about the main biological processes associated with different physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform technique based on singular value decomposition so that identifying correlated-bicluster problem from gene expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift algorithm are devised to efficiently produce \\delta-corBiclusters. The biclusters obtained using our method from gene expression data sets of multiple human organs and the yeast Saccharomyces cerevisiae demonstrate clear biological meanings.",
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"content": "With the continuous shrinking of the feature size, the effect of stress on the performance of the IC device and circuit can no longer be ignored. In fact, stress engineering is becoming more and more widely used today in advanced IC manufacture processes to improve device performance. Different from the intentionally introduced stresses to improve circuit performance, the shallow-trench-isolation (STI) stress, which is exerted by STI wells on the active area of devices, is a by-product of the fabrication process and has increasingly significant impact on the circuit behavior. This paper proposes a complete flow to characterize the influence of STI stress on the performance of RF/analog circuits by considering detailed layout and process information. An accurate and efficient finite-element method-based stress simulator has been developed to extract stress distribution from layouts of IC designs. The existing MOSFET model is also enhanced to capture the effects of stress on mobility, threshold voltage. With the enhanced model, we are able to study the influence of layout-dependent STI stress on the performance of real circuits and establish corresponding optimization strategies. The proposed flow has been applied to a series of RF/analog IC designs based on a 90-nm CMOS technology.",
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"abstract": "Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively.",
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"content": "In recent years, the detection of epistatic interactions of multiple genetic variants on the causes of complex diseases brings a significant challenge in genome-wide association studies (GWAS). However, most of the existing methods still suffer from algorithmic limitations such as single-objective optimization, intensive computational requirement, and premature convergence. In this paper, we propose and formulate an epistatic interaction multi-objective artificial bee colony algorithm based on decomposition (EIMOABC/D) to address those problems for genetic interaction detection in genome-wide association studies. First, to direct the genetic interaction detection, two objective functions are formulated to characterize various epistatic models; rank probability model is proposed to sort each population into different nondomination levels based on the fast nondominated sorting approach. After that, the mutual information based local search algorithm is proposed to guide the population search for disease model evaluations in an unbiased manner. To validate the effectiveness of EIMOABC/D, we compare EIMOABC/D against seven state-of-the-art methods on 77 epistatic models including eight small-scale epistatic models with marginal effects, eight large-scale epistatic models with marginal effects, 60 large-scale epistatic models without any marginal effect, and one case study. The experimental results indicate that our proposed algorithm EIMOABC/D outperforms seven state-of-the-art methods on those epistatic models. Furthermore, time complexity analysis and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.",
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"content": "Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.",
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"abstract": "One of the most daunting challenges confronting computational biologists is a problem that simulation developers in all disciplines face: the design of simulation code that can be easily understood and modified despite the complexity of the systems being modeled. To meet this challenge, the authors apply the discrete event system specification (DEVS), a general modeling formalism invented for the formal description of a wide range of systems that vary in time. Using DEVS, developers can address the complexity of a biological model by subdividing it into a hierarchy of simpler submodels. Hierarchical design is a well-known strategy for software development in general. But the question remains, what type of decomposition should be used? The authors use the upper levels of a hierarchy to separate different functions or algorithms, and then dedicate lower levels to the partitioning of space. To illustrate the approach, they present a DEVS-based model that captures the 3D self-assembly of vesicle clusters and their role in the propagation of information between nerve cells.",
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"content": "Small molecule(SM) drugs can affect the expression of miRNAs, which plays crucial roles in many important biological processes. The chemical structure and clinical information of small molecule can simultaneously incorporate information such as anatomical distribution, therapeutic effects and structural characteristics. It is necessary to develop a novel model that incorporates small molecule chemical structure and clinical information to reveal the unknown small molecule-miRNA associations. In this study, we developed a new framework based on non-negative matrix factorization, called SMANMF, to discover the potential small molecules-miRNAs associations. First, the functional similarity of two miRNAs can be obtained by computing the overlap of the target gene sets in which the miRNAs interact together, and we integrated two types of small molecule similarities, including chemical similarity and clinical similarity. Then, we utilized a non-negative matrix factorization model to discover the unknown relationship between small molecules and miRNAs. The evaluation results indicate that our model can achieve superior prediction performance compared with previous approaches in 5-fold cross-validation. At the same time, the results of case studies also reveal that the SMANMF model has good predictive performance for predicting the potential association between small molecules and miRNAs.",
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